The visual question answering task reasons about an image given a question. We use the VQAv2 dataset [1]}, which contains 443,757 image-question pairs in the train set and 214,354 in the validation set. We also assess the perceptiveness of models trained on `Visual Question Answering: Changing Priors' (VQA-CP) data [2]}, which was released after several studies suggested that VQAv2 models heavily rely on answer priors. For instance, `how many' questions are typically answered with `2.' To overcome this shortcoming, VQA-CP suggested a new train-test-split. As a result, the train and test sets have different prior distributions for each question type. The new split consists of 438,183 training samples, and 219,928 samples for validation.
